Lived Experiences of Chronic Pain among Immigrant Indian Women in Canada
Bibliographic record
Abstract
Chronic pain affects an estimated 1.5 million people in Canada (Canadian Pain Task Force, 2019). As recent immigrant status is a risk factor for the development and progression of chronic pain (Cimmino, Ferrone Cutolo, 2011), it is a particular concern for Canadian immigrant communities. Currently, the Indian population is the fastest growing immigrant group in the country (Statistics Canada, 2014). This makes them an important community in which to understand chronic pain – both its manifestations and its meaning – to ensure culturally-sensitive pain management (Weerasinghe Numer, 2010). As elsewhere in the world, the prevalence of chronic pain is higher among Indian women than men, with women also reporting more severe pain (Dureja et al., 2013). In countries other than India, Indian women also report more pain than non-Indian women (Chia et al., 2016; Allison et al., 2002). However, little is known about their lived experiences of chronic pain or the context in which their pain occurs. In order to fill these gaps, the current qualitative study explores: (1) Canadian immigrant Indian women’s lived experiences of chronic pain, and (2) the role culture plays in these experiences. Thirteen immigrant Indian women with chronic, non-cancer pain participated in this study. Women’s experiences were gathered using one-on-one interviews and photovoice methods. interviews and photographs were analyzed using van Manen’s (1990) phenomenological thematic analysis and Oliffe, Bottorff, Kelly and Halpin’s (2008) photograph analysis. Findings revealed the multidimensional nature of Indian women’s chronic pain, as well as the influence of the larger contexts of gender and immigration on their pain experiences. In particular, women discussed pain in relation to gendered roles and expectations. They also described the process of emigrating to a new country, adapting a new way of life, and adjusting to shifting roles as key factors affecting their pain experiences. These valuable insights have practical, as well as theoretical implications; they not only inform our current knowledge of pain within this understudied population, but also expand our understanding of pain’s biocultural components to include gender and immigration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".